{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95703c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在获取股票数据...\n",
      "成功获取真实股票数据\n",
      "数据形状: (1084, 11)\n",
      "前5行数据:\n",
      "              股票代码     open    close     high      low   volume           成交额  \\\n",
      "date                                                                            \n",
      "2015-01-05  000002  2023.02  2091.09  2140.84  2000.76  6560836  9.700712e+09   \n",
      "2015-01-06  000002  2050.51  2019.09  2101.56  1978.51  3346347  4.839616e+09   \n",
      "2015-01-07  000002  2006.00  2002.07  2037.42  1971.96  2642051  3.772151e+09   \n",
      "2015-01-08  000002  2013.85  1918.29  2020.40  1901.27  2639394  3.629554e+09   \n",
      "2015-01-09  000002  1911.74  1899.96  2000.76  1879.02  3294584  4.521978e+09   \n",
      "\n",
      "              振幅   涨跌幅     涨跌额   换手率  \n",
      "date                                  \n",
      "2015-01-05  7.15  6.75  132.22  6.76  \n",
      "2015-01-06  5.88 -3.44  -72.00  3.45  \n",
      "2015-01-07  3.24 -0.84  -17.02  2.72  \n",
      "2015-01-08  5.95 -4.18  -83.78  2.72  \n",
      "2015-01-09  6.35 -0.96  -18.33  3.39  \n",
      "数据形状: (1084, 11)\n",
      "前5行数据:\n",
      "              股票代码     open    close     high      low   volume           成交额  \\\n",
      "date                                                                            \n",
      "2015-01-05  000002  2023.02  2091.09  2140.84  2000.76  6560836  9.700712e+09   \n",
      "2015-01-06  000002  2050.51  2019.09  2101.56  1978.51  3346347  4.839616e+09   \n",
      "2015-01-07  000002  2006.00  2002.07  2037.42  1971.96  2642051  3.772151e+09   \n",
      "2015-01-08  000002  2013.85  1918.29  2020.40  1901.27  2639394  3.629554e+09   \n",
      "2015-01-09  000002  1911.74  1899.96  2000.76  1879.02  3294584  4.521978e+09   \n",
      "\n",
      "              振幅   涨跌幅     涨跌额   换手率  \n",
      "date                                  \n",
      "2015-01-05  7.15  6.75  132.22  6.76  \n",
      "2015-01-06  5.88 -3.44  -72.00  3.45  \n",
      "2015-01-07  3.24 -0.84  -17.02  2.72  \n",
      "2015-01-08  5.95 -4.18  -83.78  2.72  \n",
      "2015-01-09  6.35 -0.96  -18.33  3.39  \n",
      "\n",
      "正在构造技术指标...\n",
      "成功计算TA-Lib技术指标\n",
      "处理后的数据形状: (1025, 31)\n",
      "\n",
      "============================================================\n",
      "特征工程\n",
      "============================================================\n",
      "使用的特征数量: 15\n",
      "特征列表: ['close', 'volume', 'close-open', 'high-low', 'MA5', 'MA10', 'MA20', 'MA60', 'RSI', 'MOM', 'EMA12', 'EMA26', 'MACD', 'MACDsignal', 'MACDhist']\n",
      "特征矩阵形状: (1024, 15)\n",
      "目标变量分布: 上涨 491天, 下跌 533天\n",
      "\n",
      "训练集: 819 个样本\n",
      "测试集: 205 个样本\n",
      "\n",
      "============================================================\n",
      "多种模型性能比较\n",
      "============================================================\n",
      "\n",
      "=== 逻辑回归 ===\n",
      "准确率: 0.5268\n",
      "\n",
      "=== 随机森林 ===\n",
      "准确率: 0.5512\n",
      "\n",
      "=== 梯度提升树 ===\n",
      "准确率: 0.5122\n",
      "\n",
      "=== AdaBoost ===\n",
      "准确率: 0.5220\n",
      "\n",
      "=== SVM ===\n",
      "准确率: 0.5220\n",
      "\n",
      "=== K近邻 ===\n",
      "准确率: 0.5171\n",
      "\n",
      "=== 决策树 ===\n",
      "准确率: 0.5268\n",
      "\n",
      "============================================================\n",
      "模型性能汇总\n",
      "============================================================\n",
      "         模型       准确率\n",
      "1      随机森林  0.551220\n",
      "0      逻辑回归  0.526829\n",
      "6       决策树  0.526829\n",
      "3  AdaBoost  0.521951\n",
      "4       SVM  0.521951\n",
      "5       K近邻  0.517073\n",
      "2     梯度提升树  0.512195\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎯 最佳模型: 随机森林\n",
      "📊 最佳准确率: 0.5512\n",
      "\n",
      "详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          下跌       0.55      0.79      0.65       107\n",
      "          上涨       0.56      0.30      0.39        98\n",
      "\n",
      "    accuracy                           0.55       205\n",
      "   macro avg       0.55      0.54      0.52       205\n",
      "weighted avg       0.55      0.55      0.52       205\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "特征重要性分析\n",
      "============================================================\n",
      "随机森林特征重要性:\n",
      "            特征       重要性\n",
      "9          MOM  0.091150\n",
      "2   close-open  0.085807\n",
      "3     high-low  0.079277\n",
      "8          RSI  0.077283\n",
      "14    MACDhist  0.076133\n",
      "1       volume  0.075456\n",
      "4          MA5  0.070990\n",
      "13  MACDsignal  0.065396\n",
      "0        close  0.064128\n",
      "12        MACD  0.055885\n",
      "5         MA10  0.055170\n",
      "10       EMA12  0.055137\n",
      "7         MA60  0.050689\n",
      "11       EMA26  0.049032\n",
      "6         MA20  0.048466\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "参数调优\n",
      "============================================================\n",
      "最优参数: {'max_depth': 3, 'min_samples_leaf': 10, 'n_estimators': 50}\n",
      "最优分数: 0.5214050576088582\n",
      "调优后准确率: 0.5268\n",
      "\n",
      "============================================================\n",
      "收益回测分析\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "买入持有策略最终收益: nan%\n",
      "随机森林策略最终收益: nan%\n",
      "📉 预测策略未能跑赢买入持有策略\n",
      "\n",
      "============================================================\n",
      "模型总结与建议\n",
      "============================================================\n",
      "\n",
      "📊 股票涨跌预测分析总结:\n",
      "\n",
      "🎯 最佳模型: 随机森林\n",
      "📈 预测准确率: 0.5512\n",
      "📅 数据期间: 2015-01-01 至 2019-12-31\n",
      "💼 分析标的: 万科A(000002)\n",
      "\n",
      "💡 投资建议:\n",
      "1. 股票预测具有不确定性，模型仅供参考\n",
      "2. 建议结合基本面分析和其他技术指标\n",
      "3. 注意控制仓位和风险管理\n",
      "4. 定期更新模型以适应市场变化\n",
      "\n",
      "🚀 改进方向:\n",
      "- 添加更多技术指标和基本面数据\n",
      "- 考虑市场情绪和宏观经济因素\n",
      "- 使用更复杂的时间序列模型\n",
      "- 结合深度学习方法\n",
      "\n",
      "\n",
      "股票涨跌预测分析完成!\n"
     ]
    }
   ],
   "source": [
    "# # 8.3 量化金融 - 股票涨跌预测模型搭建（使用akshare）\n",
    "# **1.引入之后需要用到的库**\n",
    "import akshare as ak\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import talib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# **2.股票数据处理与衍生变量生成**\n",
    "print(\"正在获取股票数据...\")\n",
    "try:\n",
    "    # 使用akshare获取股票数据\n",
    "    df = ak.stock_zh_a_hist(symbol=\"000002\", period=\"daily\", start_date=\"20150101\", end_date=\"20191231\", adjust=\"hfq\")\n",
    "    \n",
    "    # 重命名列以匹配原代码\n",
    "    df = df.rename(columns={\n",
    "        '日期': 'date',\n",
    "        '开盘': 'open', \n",
    "        '最高': 'high',\n",
    "        '最低': 'low',\n",
    "        '收盘': 'close',\n",
    "        '成交量': 'volume'\n",
    "    })\n",
    "    df = df.set_index('date')\n",
    "    \n",
    "    print(\"成功获取真实股票数据\")\n",
    "    print(f\"数据形状: {df.shape}\")\n",
    "    print(\"前5行数据:\")\n",
    "    print(df.head())\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"获取数据失败: {e}\")\n",
    "    # 创建模拟数据作为备选\n",
    "    print(\"创建模拟股票数据进行演示...\")\n",
    "    dates = pd.date_range(start='2015-01-01', end='2019-12-31', freq='D')\n",
    "    np.random.seed(42)\n",
    "    price = 10 + np.cumsum(np.random.randn(len(dates)) * 0.1)\n",
    "    volume = np.random.randint(1000000, 10000000, len(dates))\n",
    "    \n",
    "    df = pd.DataFrame({\n",
    "        'open': price * (1 + np.random.randn(len(dates)) * 0.01),\n",
    "        'high': price * (1 + np.random.randn(len(dates)) * 0.02),\n",
    "        'low': price * (1 - np.random.randn(len(dates)) * 0.02),\n",
    "        'close': price,\n",
    "        'volume': volume\n",
    "    }, index=dates)\n",
    "    df.index.name = 'date'\n",
    "\n",
    "print(f\"数据形状: {df.shape}\")\n",
    "print(\"前5行数据:\")\n",
    "print(df.head())\n",
    "\n",
    "# 2.简单衍生变量构造\n",
    "print(\"\\n正在构造技术指标...\")\n",
    "df['close-open'] = (df['close'] - df['open']) / df['open']\n",
    "df['high-low'] = (df['high'] - df['low']) / df['low']\n",
    "\n",
    "df['pre_close'] = df['close'].shift(1)\n",
    "df['price_change'] = df['close'] - df['pre_close']\n",
    "df['p_change'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
    "\n",
    "# 3.移动平均线相关数据构造\n",
    "df['MA5'] = df['close'].rolling(5).mean()\n",
    "df['MA10'] = df['close'].rolling(10).mean()\n",
    "df['MA20'] = df['close'].rolling(20).mean()\n",
    "df['MA60'] = df['close'].rolling(60).mean()\n",
    "\n",
    "# 4.通过Ta_lib库构造衍生变量\n",
    "try:\n",
    "    df['RSI'] = talib.RSI(df['close'], timeperiod=12)\n",
    "    df['MOM'] = talib.MOM(df['close'], timeperiod=5)\n",
    "    df['EMA12'] = talib.EMA(df['close'], timeperiod=12)\n",
    "    df['EMA26'] = talib.EMA(df['close'], timeperiod=26)\n",
    "    df['MACD'], df['MACDsignal'], df['MACDhist'] = talib.MACD(df['close'], fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "    \n",
    "    # 添加更多技术指标\n",
    "    df['SMA'] = talib.SMA(df['close'], timeperiod=30)\n",
    "    df['WMA'] = talib.WMA(df['close'], timeperiod=30)\n",
    "    df['ADX'] = talib.ADX(df['high'], df['low'], df['close'], timeperiod=14)\n",
    "    df['ATR'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)\n",
    "    \n",
    "    print(\"成功计算TA-Lib技术指标\")\n",
    "except Exception as e:\n",
    "    print(f\"计算TA-Lib指标时出错: {e}\")\n",
    "    # 手动计算一些基本指标\n",
    "    df['RSI'] = 50 + np.random.randn(len(df)) * 10  # 模拟RSI\n",
    "    df['MOM'] = df['close'].diff(5)  # 动量\n",
    "    df['EMA12'] = df['close'].ewm(span=12).mean()\n",
    "    df['EMA26'] = df['close'].ewm(span=26).mean()\n",
    "    df['MACD'] = df['EMA12'] - df['EMA26']\n",
    "    df['MACDsignal'] = df['MACD'].ewm(span=9).mean()\n",
    "    df['MACDhist'] = df['MACD'] - df['MACDsignal']\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "print(f\"处理后的数据形状: {df.shape}\")\n",
    "\n",
    "# **3.特征变量和目标变量提取**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"特征工程\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 选择特征变量\n",
    "feature_columns = ['close', 'volume', 'close-open', 'high-low', 'MA5', 'MA10', 'MA20', 'MA60', \n",
    "                   'RSI', 'MOM', 'EMA12', 'EMA26', 'MACD', 'MACDsignal', 'MACDhist']\n",
    "\n",
    "# 只选择存在的列\n",
    "available_features = [col for col in feature_columns if col in df.columns]\n",
    "X = df[available_features]\n",
    "\n",
    "print(f\"使用的特征数量: {len(available_features)}\")\n",
    "print(f\"特征列表: {available_features}\")\n",
    "\n",
    "# 目标变量：预测下一交易日涨跌 (1:上涨, -1:下跌)\n",
    "y = np.where(df['price_change'].shift(-1) > 0, 1, -1)\n",
    "\n",
    "# 删除最后一行（因为y的shift(-1)导致最后一行没有对应的目标值）\n",
    "X = X[:-1]\n",
    "y = y[:-1]\n",
    "\n",
    "print(f\"特征矩阵形状: {X.shape}\")\n",
    "print(f\"目标变量分布: 上涨 {sum(y == 1)}天, 下跌 {sum(y == -1)}天\")\n",
    "\n",
    "# **4.训练集和测试集数据划分**\n",
    "X_length = X.shape[0]\n",
    "split = int(X_length * 0.8)  # 80%训练，20%测试\n",
    "X_train, X_test = X[:split], X[split:]\n",
    "y_train, y_test = y[:split], y[split:]\n",
    "\n",
    "print(f\"\\n训练集: {X_train.shape[0]} 个样本\")\n",
    "print(f\"测试集: {X_test.shape[0]} 个样本\")\n",
    "\n",
    "# **5.多种机器学习模型比较**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"多种模型性能比较\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 定义多个模型\n",
    "models = {\n",
    "    '逻辑回归': LogisticRegression(random_state=42, max_iter=1000),\n",
    "    '随机森林': RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42),\n",
    "    '梯度提升树': GradientBoostingClassifier(n_estimators=100, max_depth=3, random_state=42),\n",
    "    'AdaBoost': AdaBoostClassifier(n_estimators=100, random_state=42),\n",
    "    'SVM': SVC(probability=True, random_state=42),\n",
    "    'K近邻': KNeighborsClassifier(n_neighbors=5),\n",
    "    '决策树': DecisionTreeClassifier(max_depth=5, random_state=42)\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    try:\n",
    "        # 模型训练\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        # 模型预测\n",
    "        y_pred = model.predict(X_test)\n",
    "        y_pred_proba = model.predict_proba(X_test) if hasattr(model, 'predict_proba') else None\n",
    "        \n",
    "        # 评估指标\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        \n",
    "        print(f\"准确率: {accuracy:.4f}\")\n",
    "        \n",
    "        # 存储结果\n",
    "        results[model_name] = {\n",
    "            'model': model,\n",
    "            'accuracy': accuracy,\n",
    "            'y_pred': y_pred,\n",
    "            'y_pred_proba': y_pred_proba\n",
    "        }\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"训练失败: {e}\")\n",
    "\n",
    "# **6.模型性能比较**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型性能汇总\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "if results:\n",
    "    # 创建性能比较表格\n",
    "    performance_df = pd.DataFrame({\n",
    "        '模型': list(results.keys()),\n",
    "        '准确率': [result['accuracy'] for result in results.values()]\n",
    "    }).sort_values('准确率', ascending=False)\n",
    "    \n",
    "    print(performance_df)\n",
    "    \n",
    "    # 可视化模型性能比较\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    bars = plt.bar(performance_df['模型'], performance_df['准确率'], \n",
    "                   color=plt.cm.Set3(np.arange(len(performance_df))))\n",
    "    plt.title('股票涨跌预测模型性能比较', fontsize=14, fontweight='bold')\n",
    "    plt.ylabel('准确率', fontsize=12)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.grid(True, alpha=0.3, axis='y')\n",
    "    plt.ylim(0, 1.0)\n",
    "    \n",
    "    # 在柱状图上添加数值\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                f'{height:.3f}', ha='center', va='bottom', fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 选择最佳模型\n",
    "    best_model_name = performance_df.iloc[0]['模型']\n",
    "    best_model = results[best_model_name]['model']\n",
    "    best_accuracy = performance_df.iloc[0]['准确率']\n",
    "    \n",
    "    print(f\"\\n🎯 最佳模型: {best_model_name}\")\n",
    "    print(f\"📊 最佳准确率: {best_accuracy:.4f}\")\n",
    "    \n",
    "    # 使用最佳模型进行详细分析\n",
    "    best_y_pred = results[best_model_name]['y_pred']\n",
    "    \n",
    "    print(\"\\n详细分类报告:\")\n",
    "    print(classification_report(y_test, best_y_pred, target_names=['下跌', '上涨']))\n",
    "    \n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, best_y_pred)\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    plt.title(f'{best_model_name} - 混淆矩阵', fontsize=14, fontweight='bold')\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(2)\n",
    "    plt.xticks(tick_marks, ['预测下跌', '预测上涨'])\n",
    "    plt.yticks(tick_marks, ['实际下跌', '实际上涨'])\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('真实标签')\n",
    "    \n",
    "    # 在图中添加数字\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            plt.text(j, i, format(cm[i, j], 'd'),\n",
    "                    horizontalalignment=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\",\n",
    "                    fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# **7.特征重要性分析**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"特征重要性分析\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "if '随机森林' in results:\n",
    "    rf_model = results['随机森林']['model']\n",
    "    feature_importance = rf_model.feature_importances_\n",
    "    \n",
    "    # 创建特征重要性DataFrame\n",
    "    importance_df = pd.DataFrame({\n",
    "        '特征': available_features,\n",
    "        '重要性': feature_importance\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"随机森林特征重要性:\")\n",
    "    print(importance_df)\n",
    "    \n",
    "    # 可视化特征重要性\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(importance_df['特征'], importance_df['重要性'], color='skyblue')\n",
    "    plt.xlabel('特征重要性')\n",
    "    plt.title('随机森林特征重要性排序', fontsize=14, fontweight='bold')\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.grid(True, alpha=0.3, axis='x')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# **8.参数调优（使用最佳模型）**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"参数调优\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "if results:\n",
    "    # 使用随机森林进行参数调优\n",
    "    parameters = {\n",
    "        'n_estimators': [50, 100, 200],\n",
    "        'max_depth': [3, 5, 7],\n",
    "        'min_samples_leaf': [5, 10, 20]\n",
    "    }\n",
    "    \n",
    "    rf_for_tuning = RandomForestClassifier(random_state=42)\n",
    "    grid_search = GridSearchCV(rf_for_tuning, parameters, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "    \n",
    "    grid_search.fit(X_train, y_train)\n",
    "    \n",
    "    print(\"最优参数:\", grid_search.best_params_)\n",
    "    print(\"最优分数:\", grid_search.best_score_)\n",
    "    \n",
    "    # 使用调优后的参数重新训练\n",
    "    best_rf = RandomForestClassifier(**grid_search.best_params_, random_state=42)\n",
    "    best_rf.fit(X_train, y_train)\n",
    "    tuned_accuracy = accuracy_score(y_test, best_rf.predict(X_test))\n",
    "    print(f\"调优后准确率: {tuned_accuracy:.4f}\")\n",
    "\n",
    "# **9.收益回测曲线绘制**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"收益回测分析\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "if results:\n",
    "    # 使用最佳模型进行回测\n",
    "    best_y_pred = results[best_model_name]['y_pred']\n",
    "    \n",
    "    # 创建回测数据\n",
    "    backtest_df = X_test.copy()\n",
    "    backtest_df['prediction'] = best_y_pred\n",
    "    backtest_df['actual_change'] = y_test\n",
    "    \n",
    "    # 计算每日收益率\n",
    "    backtest_df['daily_return'] = (backtest_df['close'].shift(-1) - backtest_df['close']) / backtest_df['close']\n",
    "    \n",
    "    # 策略收益：根据预测进行交易\n",
    "    backtest_df['strategy_return'] = backtest_df['prediction'] * backtest_df['daily_return']\n",
    "    \n",
    "    # 计算累计收益\n",
    "    backtest_df['buy_hold_cumulative'] = (1 + backtest_df['daily_return']).cumprod()\n",
    "    backtest_df['strategy_cumulative'] = (1 + backtest_df['strategy_return']).cumprod()\n",
    "    \n",
    "    # 绘制收益曲线\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.plot(backtest_df.index, backtest_df['buy_hold_cumulative'], \n",
    "             label='买入持有策略', linewidth=2, alpha=0.8)\n",
    "    plt.plot(backtest_df.index, backtest_df['strategy_cumulative'], \n",
    "             label=f'{best_model_name}预测策略', linewidth=2, alpha=0.8)\n",
    "    plt.title('策略收益回测比较', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('日期')\n",
    "    plt.ylabel('累计收益')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    plt.gcf().autofmt_xdate()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 计算策略表现指标\n",
    "    buy_hold_final = backtest_df['buy_hold_cumulative'].iloc[-1]\n",
    "    strategy_final = backtest_df['strategy_cumulative'].iloc[-1]\n",
    "    \n",
    "    print(f\"买入持有策略最终收益: {(buy_hold_final - 1) * 100:.2f}%\")\n",
    "    print(f\"{best_model_name}策略最终收益: {(strategy_final - 1) * 100:.2f}%\")\n",
    "    \n",
    "    if strategy_final > buy_hold_final:\n",
    "        print(\"🎉 预测策略跑赢买入持有策略!\")\n",
    "    else:\n",
    "        print(\"📉 预测策略未能跑赢买入持有策略\")\n",
    "\n",
    "# **10.模型总结**\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型总结与建议\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "print(f\"\"\"\n",
    "📊 股票涨跌预测分析总结:\n",
    "\n",
    "🎯 最佳模型: {best_model_name if 'best_model_name' in locals() else 'N/A'}\n",
    "📈 预测准确率: {best_accuracy if 'best_accuracy' in locals() else 'N/A':.4f}\n",
    "📅 数据期间: 2015-01-01 至 2019-12-31\n",
    "💼 分析标的: 万科A(000002)\n",
    "\n",
    "💡 投资建议:\n",
    "1. 股票预测具有不确定性，模型仅供参考\n",
    "2. 建议结合基本面分析和其他技术指标\n",
    "3. 注意控制仓位和风险管理\n",
    "4. 定期更新模型以适应市场变化\n",
    "\n",
    "🚀 改进方向:\n",
    "- 添加更多技术指标和基本面数据\n",
    "- 考虑市场情绪和宏观经济因素\n",
    "- 使用更复杂的时间序列模型\n",
    "- 结合深度学习方法\n",
    "\"\"\")\n",
    "\n",
    "print(\"\\n股票涨跌预测分析完成!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be4c6882",
   "metadata": {},
   "outputs": [],
   "source": [
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   "cell_type": "code",
   "execution_count": null,
   "id": "51d7ca6d",
   "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
   "id": "fc6bcc8d",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7153e1d6",
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